Statins are the most prescribed drugs in the world. Their efficacy in primary and secondary prevention of cardiovascular disease as well as beneficial pleiotrophic and anti-inflammatory effects have fostered increasingly aggressive usage and dosage. Their main clinically relevant safety risk is statin-induced myopathy (SIM) evidenced clinically as a constellation of neuromuscular side effects (hereinafter NMSEs). NMSEs are disabling to 3-20% of patients on statins, require alteration of therapy, and reduce compliance. NMSEs include myalgias (muscle aches, cramps, weakness) and myositis (monitored by elevation of serum creatine kinase [CK] activity). NMSEs vary in extent between drugs and from patient to patient. We will develop a novel product termed SIM PhyzioType" system to provide clinicians with individualized information for each patient on the safest statin drug among atorvastatin, simvastatin, and rosuvastatin, the 3 most prescribed statins. The PhyzioType consists of a multi-SNP (single nucleotide polymorphism) ensemble that, interpreted with a biomathematical algorithm, predicts drug response. As part of our preliminary work, we have genotyped 242 statin-treated patients with a targeted array of 384 SNPs from 222 cardiovascular and neuromuscular candidate genes, and performed physiogenomic associations. We have developed a prototype PhyzioType system incorporating predictive models for myalgia, serum CK activity, and LDLc reduction for atorvastatin and simvastatin patients. We have discovered a mechanistic link between vascular homeostasis and CK elevation, and between serotonin receptors and myalgia. These results have been published in Pharmacogenomics and Muscle &Nerve. For this SBIR Program, the physiogenomics technology and state-of-the-art genotyping laboratories of Genomas will be combined with the clinical experience and resources of Drs. Paul Thompson, Alan Wu, and Bruce Gordon, respectively, at Hartford Hospital, Univ. California San Francisco and Rogosin Institute, through institutional subcontracts. We will recruit to obtain 250 patients treated with each drug and use existing clinical records to characterize their NMSE and LDLc responses. We will use physiogenomics to identify those SNPs that differentiate the risk of NMSEs among the 3 statins and combine them into the SIM PhyzioType system. In Phase I, we will continue genotyping with the hypothesis-driven array of 384 SNPs. In Phase II, we will incorporate a hypothesis-free approach by genotyping each patient at 550,000 SNPs with a total genome array covering all ~30,000 genes on all chromosomes and the mitochondrion. This work will also contribute to the pharmacology of SIM and unravel new pharmaceutical targets. We will create and validate the SIM PhyzioType system with clinically useful prediction of NMSEs and potency for each of the 3 statins. In Phase III a prospective trial is planned for FDA approval of the SIM PhyzioType product.